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Discrete-space kinetic models, i.e., Markov state models, have emerged aspowerful tools for reducing the complexity of trajectories generated frommolecular dynamics simulations. These models require configuration-spacerepresentations that accurately characterize the relevant dynamics.Well-established, low-dimensional order parameters for constructing thisrepresentation have led to widespread application of Markov state models tostudy conformational dynamics in biomolecular systems. On the contrary,applications to characterize single-molecule diffusion processes have beenscarce and typically employ system-specific, higher-dimensional orderparameters to characterize the local solvation state of the molecule. In thiswork, we propose an automated method for generating a coarseconfiguration-space representation, using generic features of solvationstructure---the coordination numbers about each particle. To overcome theinherent noisy behavior of these low-dimensional observables, we treat thefeatures as indicators of an underlying, latent Markov process. The resultinghidden Markov models filter the trajectories of each feature into the mostlikely latent solvation state at each time step. The filtered trajectories arethen used to construct a configuration-space discretization, which accuratelydescribes the diffusion kinetics. The method is validated on a standard modelfor glassy liquids, where particle jumps between local cages determine thediffusion properties of the system. Not only do the resulting models providequantitatively accurate characterizations of the diffusion constant, but theyalso reveal a mechanistic description of diffusive jumps, quantifying theheterogeneity of local diffusion.

Automated detection of many-particle solvation states for accurate characterizations of diffusion kinetics